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👋 Moiz, an ML Architect

I am a Machine Learning Engineer with over six years of experience in applying advanced AI and machine learning models to solve complex business problems. I specialize in designing, fine-tuning, and deploying AI models that optimize processes and enhance decision-making capabilities.

Throughout my career, I have collaborated with cross-functional teams to integrate machine learning solutions into production environments. My expertise spans a variety of domains, including natural language processing, computer vision, and time-series analysis. I am passionate about staying at the forefront of technology and continuously learning new methods to solve emerging challenges in the AI field.

Technology

BERT and Transformers, Spacy, Gensim, FastText, NLTK, OpenNLP, TensorFlow, PyTorch, Keras, OpenCV, Pandas, Numpy, Scikit Learn, Git & Version Control, Docker, Kubernetes, Python, R, Tableau, Time Series Analysis

Experience

  • Recommender Systems: Successfully implemented a collaborative filtering-based recommender system, increasing customer engagement by 20%.
  • Real-time Emotion Analysis: Developed a real-time emotion detection system using CNN-LSTM networks, achieving 92% accuracy on recognized benchmarks.
  • AI System Design & Deployment: Led the design, development, and deployment of end-to-end machine learning models to solve various business challenges, including customer segmentation, predictive analytics, and personalized recommendations.
  • Advanced Model Development: Created and fine-tuned models using TensorFlow, PyTorch, and Keras, focusing on NLP and computer vision applications.
  • Cross-functional Collaboration: Partnered with product managers, data engineers, and other stakeholders to align technical solutions with business goals.

  • Data Strategy & Automation: Collaborated on developing data strategies and automating workflows to enhance data quality and analytics efficiency.
  • AI-Driven Decision Support: Created tools to support data-driven decision-making across various business functions.

Project Details

Developed a scalable text data processing pipeline that leveraged advanced NLP techniques for effective data analysis and categorization. The pipeline was designed to handle large volumes of data efficiently, utilizing techniques such as tokenization, named entity recognition (NER), and sentiment analysis. The entire system was containerized using Docker, enabling seamless deployment across different environments. This solution significantly reduced processing time and allowed for rapid iteration and deployment of NLP models.

Built an anomaly detection system specifically designed for high-frequency time-series data. The system employed a combination of autoencoders and LSTM networks to capture temporal patterns and detect anomalies. Additionally, an Isolation Forest (iForest) algorithm was integrated to enhance the detection accuracy by identifying outliers in the dataset.

Developed a deep learning model using the U-Net architecture to perform high-precision segmentation of medical images. The segmentation results were further validated using the cellpose tool, which confirmed the robustness of the model in different medical imaging scenarios.

Engineered a multi-modal AI system that integrates both computer vision and sensor data to enhance object detection and decision-making capabilities of autonomous vehicles.

Enhanced BERT models specifically for sentiment analysis in various domains, resulting in improved classification accuracy over traditional models.

Education

  • Institute: Indian Institute of Technology, Kanpur
  • Department: Department of Management Sciences
  • Major: Production & Operations Management
  • CGPA: 8.0
  • Year: 2009-2011
  • Location: Kanpur, Uttar Pradesh, India

  • Institute: RITEE, CSVTU
  • Department: Electronics & Telecommunication Engineering
  • Major Project: Implementation of Image Processing and Image Enhancement using MATLAB.
  • CGPA: 8.5
  • Year: 2005-2009
  • Location: Raipur, Chhattisgarh, India

Certifications

  • Date of Completion: June 2021
  • Certification Provider: Stanford Online
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  • Date of Completion: April 2021
  • Certification Provider: DeepLearning.ai
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  • Date of Completion: November 2022
  • Certification Provider: DeepLearning.ai
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Research Experience & Publications

  • Institution: Sultan Qaboos University
  • Year: 2014-2015
  • Undertook a project titled: "Mediator-based order acceptance decision system under the make-to-order company."
  • Worked under the guidance of Dr. Sujan Piya, focusing on improving order acceptance mechanisms.

  • Company: Central UP Gas Limited
  • Year: 2010
  • Market Analysis: Led an initiative to examine the market potential for natural gas in the Rania & Jainpur Industrial Areas.
  • Formulated strategies to establish Piped Natural Gas service stations, enhancing the distribution network.

  • Sharma, R. R. K., & Ali, S. M. (2017). Reducing a Lot Sizing Problem with Set up, Production, Shortage and Inventory Costs to Lot Sizing Problem with Set up, Production and Inventory Costs. American Journal of Operations Research, 7, 282-284. Link
  • Ali, S. M., Sharma, R.R.K., & Gupta, O.K. (2015). Lagrangian Relaxation Procedure for the Capacitated Dynamic Lot Sizing Problem. AIMS International Conference on Management. Link
  • Syed, M. A., & Sharif. (2012). Aggregate Planning for Semi-finished goods under make-to-stock environment. International Journal of Advances in Management, Technology & Engineering Sciences, 1(8(I)), 104-107.
  • Sharif, & Syed, M.A. (2012). Procurement Policies & Inventory Management System in Manufacturing and Service Settings: An Optimization Framework. International Journal of Business, Management & Social Sciences, 1(9), 27-32.
Send an email to communicate.

Get in Touch

Syed Moiz Ali

9984673534

moizeali@gmail.com

Hyderabad, India

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